防灾减灾工程学报2025，Vol.45Issue(4)：776-783,8.DOI:10.13409/j.cnki.jdpme.20231108005
改进U-Net模型的隧道掌子面图像语义分割研究
Semantic Segmentation of Tunnel Handheld Noodle Rock Mass Structure Images with Improved U-Net Model
摘要
Abstract
The structural characteristics of the rock mass exposed at the tunnel face provide a direct ba-sis for assessing geotechnical conditions,formulating construction and support strategies,and mitigat-ing risks of accidents such as collapses and water inrush.When applying the U-Net model to the seg-mentation and recognition of tunnel face rock mass structure images,the downsampling process can lead to the loss of fine details in the rock mass,while the skip connections used during upsampling to transfer low-level features to higher levels may cause excessively large feature maps.To address these issues,an improved U-Net model is proposed by incorporating the Atrous Spatial Pyramid Pooling(ASPP)module and the Convolutional Block Attention Module(CBAM).Specifically,the ASPP is integrated into the skip connections of the U-Net model to capture multi-scale contextual information through atrous convolutions with varying dilation rates,enabling the fusion of features from diverse re-ceptive fields for a more comprehensive understanding of image content.Concurrently,the CBAM is embedded into the downsampling process of the U-Net model to enhancing the network focus more on useful features,thereby enhancing the representation capability of the extracted features.Experimental results demonstrate that the improved network model significantly outperforms the original U-Net in both segmentation and recognition performance.Evaluated on a tunnel face rock mass image dataset from a specific engineering project,the improved model achieves a Precision of 93.04%,mean Inter-section over Union(mIoU)of 74.98%,and a mean Pixel Accuracy(mPA)of 78.89%.关键词
隧道掌子面/图像语义分割/卷积注意力模块/空洞空间卷积池化金字塔模块Key words
tunnel palm-leaf noodles/image semantic segmentation/convolutional attention module/dilated spatial pyramid pooling module分类
交通工程引用本文复制引用
陈登峰,程静,赵蕾,何拓航..改进U-Net模型的隧道掌子面图像语义分割研究[J].防灾减灾工程学报,2025,45(4):776-783,8.基金项目
陕西省自然科学基础研究计划面上项目(2024JC-YBMS-286)、西安市科技计划项目(2023JH-GXRC-0216,2024JH-KGDW-0112)、前沿交叉领域培育专项项目(X20230072)资助 （2024JC-YBMS-286）
